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- W4320059944 abstract "Emergency rescue scenarios are considered to be high-risk scenarios. Using a micro air vehicle (MAV) swarm to explore the environment can provide valuable environmental information. However, due to the absence of localization infrastructure and the limited on-board capabilities, it's challenging for the low-cost MAV swarm to maintain precise localization. In this paper, a collaborative localization system for the low-cost heterogeneous MAV swarm is proposed. This system takes full advantage of advanced MAV to effectively achieve accurate localization of the heterogeneous MAV swarm through collaboration. Subsequently, H-SwarmLoc, a reinforcement learning-based planning method is proposed to plan the advanced MAV with a non-myopic objective in real-time. The experimental results show that the localization performance of our method improves 40% on average compared with baselines." @default.
- W4320059944 created "2023-02-12" @default.
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- W4320059944 date "2022-11-06" @default.
- W4320059944 modified "2023-10-13" @default.
- W4320059944 title "H-SwarmLoc" @default.
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- W4320059944 doi "https://doi.org/10.1145/3560905.3568432" @default.
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